Studying the Performance of the Computer Aided Detection (CAD) systems for the micro-calcifications detection in digital mammograms

Breast cancer is the most widespread types of cancer among women. An efficient diagnosis in its early stage can give women a better chance of full recovery. Calcification is the important sign for early breast cancer detection. Mammography is the most effective method for breast cancer early detection using low radiation doses. The studies improved the sensitivity of mammogram from 15% to 30% based on Computer Auto-Detection CAD systems, which are used as a “second opinion” to alert the radiologist to structures that, otherwise, might be overlooked. This article summarizes the various methods adopted for micro-calcification cluster detection and compares their performance. Moreover, reasons for the adoption of a common public image database as a test bench for CAD systems, motivations for further CAD tool improvements, and the effectiveness of various CAD systems in a clinical environment are given.

Using Top-Hat and Bottom-Hat Transforms for Contrast Enhancement of Mammograms

Mammography is widely used technique for breast cancer screening. There are various other techniques for breast cancer screening but mammography is the most reliable and effective technique. The images obtained through mammography are of low contrast which causes problem for the radiologists to interpret. Hence, a high quality image is mandatory for the processing of the image for extracting any kind of information. Many contrast enhancement algorithms have been developed over the years. This work presents a method to enhancement Microcalcifications in digitized mammograms. The method is based Mainly on the combination of Image Processing. The top-Hat and bottom–hat transforms are a techniques based on Mathematical morphology operations. This algorithm has been tested on mini-Mias database which have three types of breast tissues . For evaluation of performance of image enhancement algorithm, the Contrast Improvement Index (CII) and Peak Signal to Noise Ratio (PSNR) have been used. Experimental results suggest that algorithm can be improve significantly overall detection of the Computer-Aided Diagnosis (CAD) system especially for dense breast.

Automatic detection of breast lesions in mammograms images with features extraction using Otsu's method

A mammogram is the best option for early detection of breast cancer, Computer Aided Diagnostic systems(CADs) developed in order to improve the diagnosis of mammograms. This paper presents a proposed method to automatic images segmentation depending on the Otsu's method in order to detect microcalcifications and mass lesions in mammogram images. The proposed technique is based on three steps: (a) region of interest (ROI), (b) 2D wavelet transformation, and (c) OTSU thresholding application on ROI. The method tested on standard mini- MIAS database. It implemented within MATLAB software environment. Experimental results and performance evaluate results show that the proposed detection algorithm is a tool to help improve the diagnostic performance, and has the possibility and the ability to detect the breast lesions.